In the field of logistics, technology which enables the automation of cargo handling and assortment of packages is being developed. When the objects are lifted for transportation, the gripping locations need to be determined using images taken by cameras. Since the objects are not always aligned, the correct gripping locations have to be detectable, regardless of the alignments and the directions. Also, the gripping locations have to be detectable for different shapes of objects.
One way of detecting the gripping location is the use of models learned by methods such as deep learning or the like, for estimating the corresponding region within the image. Training data with few hundred to millions of annotated images need to be prepared, to generate a model which can recognize images by learning. The annotated images are prepared by labeling the areas that need to be detected, for each image. Since the segmentation of images into regions has been done manually, a long period of time was needed to prepare the training data. Also, the quality of data varied depending on the images. Therefore, it was not easy to add an object with a different shape into the scope of image recognition.